Run an OLS regression with Pandas Data Frame

0 votes

I have a pandas data frame and I would like to able to predict the values of column A from the values in columns B and C. Here is a toy example:

import pandas as pd
df = pd.DataFrame({"A": [10,20,30,40,50], 
                   "B": [20, 30, 10, 40, 50], 
                   "C": [32, 234, 23, 23, 42523]})

Ideally, I would have something like ols(A ~ B + C, data = df) but when I look at the examples from algorithm libraries like scikit-learn it appears to feed the data to the model with a list of rows instead of columns. This would require me to reformat the data into lists inside lists, which seems to defeat the purpose of using pandas in the first place. What is the most pythonic way to run an OLS regression (or any machine learning algorithm more generally) on data in a pandas data frame?

Mar 26 in Machine Learning by Dev
• 6,000 points
25 views

1 answer to this question.

0 votes

I believe you may almost achieve your desired result by utilising the statsmodels package, which was one of pandas' optional dependencies prior to version 0.20.0. (it was used for a few things in pandas.stats.)

import pandas as pd
import statsmodels.formula.api as sm
data = pd.DataFrame({"A": [10,20,30,40,50], "B": [20, 30, 10, 40, 50], "C": [32, 234, 23, 23, 42523]})
result = sm.ols(formula="A ~ B + C", data).fit()
print(result.params)
Intercept    14.952480
B             0.401182
C             0.000352
dtype: float64
 print(result.summary())
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      A   R-squared:                       0.579
Model:                            OLS   Adj. R-squared:                  0.158
Method:                 Least Squares   F-statistic:                     1.375
Date:                Thu, 14 Nov 2013   Prob (F-statistic):              0.421
Time:                        20:04:30   Log-Likelihood:                -18.178
No. Observations:                   5   AIC:                             42.36
Df Residuals:                       2   BIC:                             41.19
Df Model:                           2                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept     14.9525     17.764      0.842      0.489       -61.481    91.386
B              0.4012      0.650      0.617      0.600        -2.394     3.197
C              0.0004      0.001      0.650      0.583        -0.002     0.003
==============================================================================
Omnibus:                          nan   Durbin-Watson:                   1.061
Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.498
Skew:                          -0.123   Prob(JB):                        0.780
Kurtosis:                       1.474   Cond. No.                     5.21e+04
==============================================================================

Warnings:
[1] The condition number is large, 5.21e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
answered Apr 4 by Nandini
• 5,480 points

Related Questions In Machine Learning

0 votes
1 answer

Can I draw a regression line and show parameters using scatterplot with a pandas dataframe?

There isn't a parameter for DataFrame.plot(). Seaborn, ...READ MORE

answered Mar 7 in Machine Learning by Dev
• 6,000 points
106 views
0 votes
1 answer
0 votes
1 answer
0 votes
2 answers
+1 vote
2 answers

how can i count the items in a list?

Syntax :            list. count(value) Code: colors = ['red', 'green', ...READ MORE

answered Jul 7, 2019 in Python by Neha
• 330 points

edited Jul 8, 2019 by Kalgi 2,753 views
0 votes
1 answer
0 votes
1 answer

Linear regression returning bad fit with large x values

To make the date values start at ...READ MORE

answered Mar 23 in Machine Learning by Nandini
• 5,480 points
27 views
webinar REGISTER FOR FREE WEBINAR X
Send OTP
REGISTER NOW
webinar_success Thank you for registering Join Edureka Meetup community for 100+ Free Webinars each month JOIN MEETUP GROUP